Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study

A mixed-integer, non-linear model is developed for designing robust global supply chain networks under uncertainty. Six resilience strategies are proposed to mitigate the risk of correlated disruptions. In addition, an efficient parallel Taguchi-based memetic algorithm is developed that incorporates a customized hybrid parallel adaptive large neighborhood search. Fitness landscape analysis is used to determine an effective selection of neighborhood structures, while the upper bound found by Lagrangian relaxation heuristic is used to evaluate quality of solutions and effectiveness of the proposed metaheuristic. The model is solved for a real-life case of a global medical device manufacturer to extract managerial insights.     

Task selection and routing problems in collaborative truckload transportation

This paper introduces the task selection and routing problem in collaborative transportation in which a truckload carrier receives tasks from shippers and other partners and makes a selection between a private vehicle and an external carrier to serve each task. The objective is to minimize the variable and fixed costs for operating the private fleet plus the total costs charged by the external carrier. The mathematical formulation and the lower bound are established. A memetic algorithm is developed to solve the problem. The computational results show that the proposed algorithm is effective and efficient.     

The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics

An algorithm that can tackle time dependent vehicle routing problems with hard or soft time windows without any alteration in its structure is presented. Analytical and experimental results indicate that average computational time increases proportionally to the number of customers squared. New replicable test problems that capture the typical speed variations of congested urban settings are proposed. Solution quality, time window perturbations, and computational time results are discussed as well as a method to study the impact of perturbations by problem type. The algorithm efficiency and simplicity is well suited for urban areas where fast running times may be required.     

Supply chain network competition in price and quality with multiple manufacturers and freight service providers

In this paper, we develop both static and dynamic supply chain network models with multiple manufacturers and freight service providers competing on price and quality. The manufacturers compete with one another in terms of price and quality of the product manufactured, whereas the freight service providers compete on price and quality of the transportation service they provide for multiple modes. Both manufacturers and freight service providers maximize their utilities (profits) while considering the consequences of the competitors’ prices and quality levels. Bounds on prices and quality levels are included that have relevant policy-related implications. The governing equilibrium conditions of the static model are formulated as a variational inequality problem. The underlying dynamics are then described, with the stationary point corresponding to the variational inequality solution. An algorithm which provides a discrete-time adjustment process and tracks the evolution of the quality levels and prices over time is proposed, and convergence results given. Numerical examples illustrate how such a supply chain network framework, which is relevant to products ranging from high value to low value ones, can be applied in practice.     

A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced Progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

Accounting for population exposure to vehicle-generated pollutants and environmental equity in the toll design problem

The emissions generated by motor vehicles remain a major source of air pollutants that affect public health and contribute to anthropogenic climate change. These negative externalities can be reduced, in part, with the implementation of environmentally oriented road pricing schemes, which can be designed using optimization-based approaches. In this paper, a toll design problem is proposed for determining toll locations and levels that minimize the expected human exposure to air pollutants and the related environmental inequalities, subject to constraints on pollutant concentration levels and implementation costs. The practical use of the proposed problem is hindered in most real-world applications by the computational costs associated with the evaluation of candidate solutions, as is common for network design problems. Furthermore, the problem cannot be expressed analytically given the multiple types of models (e.g., traffic assignment, emissions, air dispersion models) that would be required to evaluate a single design alternative. For these reasons, a derivative-free surrogate-based solution algorithm is proposed for mixed integer problems like the ones considered here. Numerical examples are used to illustrate possible applications of the proposed model and to test the performance of the surrogate-based algorithm. Relative to a joint simulated annealing-genetic algorithm heuristic and a genetic algorithm-based approach, the proposed algorithm found better solutions in fewer function evaluations.     

A milk collection problem with blending

A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qualities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch-and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.     

Ant colony optimization for disaster relief operations

This paper presents a meta-heuristic of ant colony optimization (ACO) for solving the logistics problem arising in disaster relief activities. The logistics planning involves dispatching commodities to distribution centers in the affected areas and evacuating the wounded people to medical centers. The proposed method decomposes the original emergency logistics problem into two phases of decision making, i.e., the vehicle route construction, and the multi-commodity dispatch. The sub-problems are solved in an iterative manner. The first phase builds stochastic vehicle paths under the guidance of pheromone trails while a network flow based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. The performance of the algorithm is tested on a number of randomly generated networks and the results indicate that this algorithm performs well in terms of solution quality and run time.     

Continuum modeling approach to the spatial analysis of air quality and housing location choice

Today, air pollution is a great issue, and the transport sector is an important emission source. In this study, we present an integrated land use, transport, and environment model in which transport-related pollutants are assumed to influence people's housing location choices, and a continuum modeling approach is applied. The pollutants generated by the transport sector are dispersed by the wind and they affect air quality. The air quality changes people's housing choices, which in turn changes their travel behavior. We assume that the road users are continuously distributed over the city, that the road network is relatively dense, and that this network can be approximated as a continuum. The total demand is categorized into several classes, and the modeled region contains several subdistricts. People who live in different subdistricts or who belong to different classes of commuters are assumed to have different perceptions of travel time, air quality, and the housing provision–demand relationship. The finite element method and the Newton–Raphson algorithm are adopted to solve this problem, and a numerical valuation is given to illustrate the effectiveness and efficiency of the proposed model.     

Branch-and-price algorithm for the location-routing problem with time windows

This study proposes a branch-and-price algorithm to solve the Location-Routing Problem with Time Windows (LRPTW) which has never been attempted with the exact solutions before. The problem is solved by the simplex algorithm in the master problem and elementary shortest path problems with resource constraint corresponding to column generation in the subproblem until only the non-negative reduced cost columns remain. The proposed algorithm can solve many testing instances effectively. The computational results and the effect of time windows are also compared and discussed.     

An agile approach for supply chain modeling

This paper proposes the generic label correcting (GLC) algorithm incorporated with the decision rules to solve supply chain modeling problems. The rough set theory is applied to reduce the complexity of data space and to induct decision rules. This proposed approach is agile because by combining various operators and comparators, different types of paths in the reduced networks can be solved with one algorithm. Furthermore, the four cases of the supply chain modeling are illustrated.     

Aircraft grouping based on improved divisive hierarchical clustering algorithm

Because air traffic control efficiency can be largely increased by means of group, the aircraft grouping problem was studied based on improved divisive hierarchical clustering algorithm (DHCA). The concepts of position similarity, velocity similarity and heading similarity were defined and the merging rule about position matrix, velocity matrix and heading matrix was put forward. According to the definition of the most similar point and the neighbor set, the end condition of DHCA was brought forward. In order to increase the search efficiency, the neighbor set was saved by the open hash table. The flow of the improved DHCA was set up by binary tree and open hash table. The simulation results show that the improved DHCA can finish aircraft grouping, and moreover, the time complexity analysis proves that the improved DHCA has a better time efficiency than the bisection method.     

A tabu search heuristic for the local container drayage problem under a new operation mode

This paper examines the Local Container Drayage Problem (LCDP) under an operation mode in which a tractor can be detached from its companion trailer and assigned to a new task. We have incorporated a set of temporal constraints into the classical VRP to realize this operation by utilizing the idle time available to tractors and coordinating the empty containers moving between customers. A tabu search algorithm is proposed. Some numerical experiments are conducted to assess the performance of the proposed algorithm, quantify the benefit of the new operation mode, and identify the conditions under which the mode is effective.     

FIPIA with information entropy: A new hybrid method to assess airline service quality

This study proposes “FIPIA with information entropy” as a new, hybrid method to assess airline service quality by identifying the most important priorities for airline passengers and producing recommendations to airline management for optimal resource allocation to improve service quality and customer satisfaction. The proposed method is an improvement over IPA, IPIA and FIPA methods, through the introduction of information entropy and fuzzy logic to the analysis of importance, performance and impact dimensions of airline service quality to improve interpretability and actionability of analysis results. This study also offers airline managers a list of what they should improve in resource allocation in order to increase service quality considering customer satisfaction and create value by managing the relational capital more effectively. The new hybrid method was field-tested by administering a 26-item questionnaire to passengers of a major airline operator, analyzing the responses using the Importance-Performance-Impact Analysis (IPIA) method, fuzzy logic and information entropy. The analysis revealed four main dimensions of airline service quality, namely reliability, assurance, tangibles, empathy and responsiveness with 17 constituent attributes. The case study revealed that (1) resource allocation was adequate only on four attributes; (2) seven service quality attributes were identified as needing further management focus on resource allocation; (3) six service quality attributes received more resources than necessary which should be shifted to other attributes; (4) dimensions of reliability and tangibles needed more focus than others. The proposed hybrid method of FIPIA with information entropy can be employed for any industry where service quality depends on multiple attributes.     

Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach

This study addresses the problem of wasted transportation capacity in Personal Rapid Transit (PRT) systems. We propose a two-tier transportation model that integrates PRT and capillary transportation systems. We study a related multi-objective empty vehicle redistribution problem that attempts to minimize empty movement and the number of vehicles used. Furthermore, we design a hybrid multi-objective genetic algorithm that integrates multiple crossover operators and linear programming techniques to solve the proposed problem. Evaluations indicate that our algorithm produces satisfactory results, and simulations confirm the efficiency of our proposed two-tier transportation system.     

Integrating berth allocation and quay crane assignments

In this study, a dynamic allocation model using objective programming for berth allocation and quay crane assignments was preliminarily developed based on rolling-horizon approach. Afterwards, a hybrid parallel genetic algorithm (HPGA), which combined parallel genetic algorithm (PGA) and heuristic algorithm, was employed to resolve the proposed model. Furthermore, a simulation was conducted to evaluate the HPGA and to execute relevant gene repair techniques. Eventually, the numerical experiments on a specific container terminal were applied to illustrate the proposed models and algorithms. In so doing, the effectiveness of the proposed approach was verified.     

Robust schedule design for liner shipping services

This paper examines the design of liner ship route schedules that can hedge against the uncertainties in port operations, which include the uncertain wait time due to port congestion and uncertain container handling time. The designed schedule is robust in that uncertainties in port operations and schedule recovery by fast steaming are captured endogenously. This problem is formulated as a mixed-integer nonlinear stochastic programming model. A solution algorithm which incorporates a sample average approximation method, linearization techniques, and a decomposition scheme, is proposed. Extensive numerical experiments demonstrate that the algorithm obtains near-optimal solutions with the stochastic optimality gap less 1.5% within reasonable time.     

Integration of inventory and transportation decisions in a logistics system

This paper addresses some of the challenges faced by a company which is responsible for delivering coal to its four subsidiaries situated along a river, through river hired or self-owned vessels. We propose to adopt a vendor managed inventory concept that involves establishment of a central warehouse at the port, and apply the Markov Decision Process (MDP) to formulate both ordering and delivery problems, considering different transportation modes, costs, and inventory issues. An efficient algorithm is developed for solving the MDP models. Our computational tests show that the proposed strategy can significantly reduce the overall system costs while maintaining smooth Just-in-Time supplies of coal to the subsidiaries.     

Robust schedule design for liner shipping services

This paper examines the design of liner ship route schedules that can hedge against the uncertainties in port operations, which include the uncertain wait time due to port congestion and uncertain container handling time. The designed schedule is robust in that uncertainties in port operations and schedule recovery by fast steaming are captured endogenously. This problem is formulated as a mixed-integer nonlinear stochastic programming model. A solution algorithm which incorporates a sample average approximation method, linearization techniques, and a decomposition scheme, is proposed. Extensive numerical experiments demonstrate that the algorithm obtains near-optimal solutions with the stochastic optimality gap less 1.5% within reasonable time.